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Method for model-based denoising of images and image sequences

Publishing Venue

Abstract

Disclosed is a method for model-based denoising of images and image sequences. Benefits include improved functionality and improved reliability.

Country

United States

Language

English (United States)

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Method for model-based denoising of
images and image sequences

Disclosed is a method for model-based denoising of images and
image sequences. Benefits include improved
functionality and improved reliability.

Background

� � � � � Image denoising
provides the user with an enhanced, less-noisy image that shows faint
structures more clearly than in the raw image sequence. For single images,
denoising is limited to the spatial domain and various techniques exist to
remove noise by spatially averaging image pixels in local neighborhoods.
However, standard isotropic smoothing calculates the brightness at individual
image points (pixels) as the weighted average across a local neighborhood
surrounding the pixels. Although this technique reduces the noise variance, it
also causes brightness patterns to be blurred and smeared out. In particular,
in low signal-to-noise (S/N) applications, such as focused ion beam (FIB)
images of silicon structures, the faint brightness patterns of interest are
lost.

� � � � � Blurring of
brightness structures can be reduced by using anisotropic diffusion filtering
for directional, structure-preserving denoising. The underlying idea is to
replace isotropic smoothing filter kernels by directional sensitive kernels
that are adapted to local image structure. These techniques average pixels
predominantly along iso-brightness contours and avoid averaging across edge
features in the image. Several algorithmic approaches to this problem exist.

� � � � � For example, two
noisy images (see Figure 1, a and c) can be denoised using robust, anisotropic
smoothing (see Figure 1, b and d). Let f(x,y) be a noisy image over the
spatial coordinates x and y. The goal of image denoising is to
find a reconstructed (denoised) image g(x,y) over the same spatial
domain. Given the value of f and a set of criteria, g can be
calculated by minimizing the following objective function, E(g,f), with
respect to g:

� � � � � While this and
similar techniques yield significant improvement of signal-to-noise ratio in
images, these techniques are not exploiting model-based information provided by
the application domain. For this reason conventional techniques for anisotropic
diffusion filtering still suffer from the following problems: